OktoBLAS Enterprise Savings Analysis
💰 Cost, Energy & Time Savings for Organizations
This document presents a comprehensive analysis of potential savings when using OktoBLAS compared to standard PyTorch/cuBLAS implementations.
📊 Performance Baseline
Measured Performance Gains (RTX 4070 Laptop)
| Operation |
PyTorch |
OktoBLAS |
Improvement |
| GEMM FP16 1024×1024 |
30.0 TF |
33.9 TF |
+13.1% |
| GEMM FP16 2048×2048 |
33.7 TF |
40.6 TF |
+20.6% |
| GEMM FP16 4096×4096 |
40.1 TF |
42.1 TF |
+5.0% |
| Fused Attention |
0.28 TF |
1.06 TF |
3.8x |
Estimated Training Speedup
| Mode |
Speedup |
| GEMM-only optimization |
+4% |
| With Fused Attention |
+12% |
| OktoEngine Native (full stack) |
+20% |
🖥️ Hardware Configurations
Consumer/Workstation GPUs
| GPU |
TDP |
MSRP |
FP16 Tensor |
| RTX 4070 Laptop |
140W |
$1,200 |
184 TFLOPS |
| RTX 4090 |
450W |
$1,800 |
330 TFLOPS |
| RTX 6000 Ada |
300W |
$6,800 |
280 TFLOPS |
Data Center GPUs
| GPU |
TDP |
Price |
FP16 Tensor |
| A100 80GB |
400W |
$15,000 |
312 TFLOPS |
| H100 80GB |
700W |
$30,000 |
989 TFLOPS |
| H200 |
700W |
$40,000 |
989 TFLOPS |
💵 Savings Analysis by Scale
Assumptions
- Electricity cost: $0.15/kWh (global average)
- Utilization: 24/7 (720 hours/month)
- OktoBLAS speedup: +12% (with Fused Attention)
🏠 Startup / Individual (1-4 GPUs)
RTX 4070 Setup (1 GPU)
| Metric |
PyTorch |
OktoBLAS |
Savings |
| Time for 1M steps |
100 hours |
89 hours |
11 hours |
| Energy/year |
1,210 kWh |
1,077 kWh |
133 kWh |
| Cost/year |
$181 |
$162 |
$19/year |
| CO₂/year |
484 kg |
431 kg |
53 kg |
RTX 4090 Setup (4 GPUs)
| Metric |
PyTorch |
OktoBLAS |
Savings |
| Time for 1M steps |
100 hours |
89 hours |
11 hours |
| Energy/year |
15,552 kWh |
13,841 kWh |
1,711 kWh |
| Cost/year |
$2,333 |
$2,076 |
$257/year |
| CO₂/year |
6.2 ton |
5.5 ton |
0.7 ton |
5-Year Savings: $1,285
🏢 Small/Medium Business (8-32 GPUs)
RTX 6000 Ada Cluster (8 GPUs)
| Metric |
PyTorch |
OktoBLAS |
Savings |
| GPU-hours saved/year |
— |
— |
7,406 hours |
| Energy/year |
20,736 kWh |
18,455 kWh |
2,281 kWh |
| Cost/year |
$3,110 |
$2,768 |
$342/year |
| CO₂/year |
8.3 ton |
7.4 ton |
0.9 ton |
A100 Cluster (32 GPUs)
| Metric |
PyTorch |
OktoBLAS |
Savings |
| GPU-hours saved/year |
— |
— |
29,622 hours |
| Energy/year |
110,592 kWh |
98,427 kWh |
12,165 kWh |
| Cost/year |
$16,589 |
$14,764 |
$1,825/year |
| CO₂/year |
44.2 ton |
39.4 ton |
4.8 ton |
5-Year Savings (32x A100): $9,125
🏭 Enterprise (64-256 GPUs)
H100 Cluster (64 GPUs)
| Metric |
PyTorch |
OktoBLAS |
Savings |
| GPU-hours saved/year |
— |
— |
59,246 hours |
| Energy/year |
387,072 kWh |
344,494 kWh |
42,578 kWh |
| Cost/year |
$58,061 |
$51,674 |
$6,387/year |
| CO₂/year |
154.8 ton |
137.8 ton |
17.0 ton |
5-Year Savings: $31,935
H100 Cluster (256 GPUs)
| Metric |
PyTorch |
OktoBLAS |
Savings |
| GPU-hours saved/year |
— |
— |
236,983 hours |
| Energy/year |
1,548,288 kWh |
1,377,976 kWh |
170,312 kWh |
| Cost/year |
$232,243 |
$206,696 |
$25,547/year |
| CO₂/year |
619.3 ton |
551.2 ton |
68.1 ton |
5-Year Savings: $127,735
🌐 Mega Enterprise / Hyperscaler (1000+ GPUs)
H100/H200 Mega Cluster (1024 GPUs)
| Metric |
PyTorch |
OktoBLAS |
Savings |
| GPU-hours saved/year |
— |
— |
947,934 hours |
| Energy/year |
6,193,152 kWh |
5,511,906 kWh |
681,246 kWh |
| Cost/year |
$928,973 |
$826,786 |
$102,187/year |
| CO₂/year |
2,477 ton |
2,205 ton |
272 ton |
5-Year Savings: $510,935
Extreme Scale (4096 GPUs)
| Metric |
PyTorch |
OktoBLAS |
Savings |
| GPU-hours saved/year |
— |
— |
3,791,734 hours |
| Energy/year |
24,772,608 kWh |
22,047,624 kWh |
2,724,984 kWh |
| Cost/year |
$3,715,891 |
$3,307,144 |
$408,747/year |
| CO₂/year |
9,909 ton |
8,819 ton |
1,090 ton |
5-Year Savings: $2,043,735 🔥
☁️ Cloud Cost Savings
AWS/GCP/Azure Pricing Reference
| Instance |
GPUs |
On-Demand |
Spot |
| p4d.24xlarge |
8x A100 |
$32.77/hr |
~$12/hr |
| p5.48xlarge |
8x H100 |
$98.32/hr |
~$35/hr |
Cloud Savings Calculator
Single Training Job (100 hours)
| Platform |
PyTorch |
OktoBLAS |
Savings |
| 8x A100 On-Demand |
$3,277 |
$2,917 |
$360 |
| 8x H100 On-Demand |
$9,832 |
$8,750 |
$1,082 |
| 8x A100 Spot |
$1,200 |
$1,068 |
$132 |
| 8x H100 Spot |
$3,500 |
$3,115 |
$385 |
Annual Cloud Spend (10 jobs/month)
| Platform |
PyTorch |
OktoBLAS |
Savings |
| 8x A100 On-Demand |
$393,240 |
$350,040 |
$43,200/year |
| 8x H100 On-Demand |
$1,179,840 |
$1,050,000 |
$129,840/year 🔥 |
🌱 Environmental Impact
CO₂ Reduction (5 Years)
| Scale |
CO₂ Saved |
Equivalent |
| 4 GPUs |
3.5 ton |
145 trees |
| 64 GPUs |
85 ton |
3,500 trees |
| 256 GPUs |
340 ton |
14,000 trees |
| 1024 GPUs |
1,360 ton |
56,000 trees |
| 4096 GPUs |
5,450 ton |
224,000 trees |
📋 Executive Summary
Key Takeaways
|
|
| Performance |
+13% to +21% faster GEMM, 3.8x faster Attention |
| Training Speedup |
+12% overall (with Fused Attention) |
| ROI |
∞ (OktoBLAS is FREE) |
| Break-even |
Immediate (zero cost) |
Savings by Scale (5 Years)
| Scale |
GPUs |
Total Savings |
| Startup |
1-4 |
$100 - $1,300 |
| SMB |
8-32 |
$1,700 - $9,100 |
| Enterprise |
64-256 |
$32,000 - $128,000 |
| Mega Enterprise |
1024+ |
$500,000+ |
Cloud Savings (Annual)
| Workload |
Savings |
| Light (2 jobs/month) |
$8,600 - $26,000 |
| Medium (10 jobs/month) |
$43,000 - $130,000 |
| Heavy (50 jobs/month) |
$215,000 - $650,000 |
🚀 Getting Started
pip install oktoblas
import oktoblas as ob
ob.info()
ob.benchmark("gemm_fp16", 2048)
OktoBLAS — Save Time, Energy & Money
Free forever. Zero dependencies. Maximum performance.
